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Unsloth Qwen3 Embedding (0 6B)

Qwen3 Embedding (0 6B)

unsloth-notebooksunslothoriginal_template
alph-notebooks/unsloth-notebooks / Qwen3_Embedding_(0_6B).ipynb

News

Placeholder

Installation

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Unsloth

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🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!

config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]
modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]
==((====))==  Unsloth 2026.1.4: Fast Qwen3 patching. Transformers: 4.56.2.
   \\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.9.0+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.5.0
\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.33.post1. FA2 = False]
 "-____-"     Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!

model.safetensors:   0%|          | 0.00/1.19G [00:00<?, ?B/s]
tokenizer_config.json: 0.00B [00:00, ?B/s]
vocab.json: 0.00B [00:00, ?B/s]
merges.txt: 0.00B [00:00, ?B/s]
tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]
config.json:   0%|          | 0.00/313 [00:00<?, ?B/s]

We now add LoRA adapters so we only need to update a small amount of parameters!

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Unsloth: Making `model.base_model.model` require gradients

Data Prep

We now use the electroglyph/technical dataset.

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README.md:   0%|          | 0.00/512 [00:00<?, ?B/s]
dataset.jsonl: 0.00B [00:00, ?B/s]
Generating train split:   0%|          | 0/106628 [00:00<?, ? examples/s]

Let's take a look at the dataset structure:

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Dataset examples:
{'anchor': '.308', 'positive': 'The .308 Winchester is a popular rifle cartridge used for hunting and target shooting.'}
{'anchor': '.308', 'positive': 'Many precision rifles are chambered in .308 for its excellent long-range accuracy.'}
{'anchor': '.308', 'positive': 'The sniper selected a .308 caliber round for the mission.'}
{'anchor': '.338 lapua', 'positive': 'The .338 Lapua Magnum is a high-powered cartridge designed for extreme long-range shooting.'}
{'anchor': '.338 lapua', 'positive': 'Military snipers often use .338 Lapua for engagements beyond 1000 meters.'}
{'anchor': '.338 lapua', 'positive': 'The rifle was chambered in .338 Lapua for maximum effective range.'}

Baseline Inference

Let's test the base model before fine-tuning to see how it performs on our specific domain.

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--- Pre-Training Results for query: 'apexification' ---
0.5435 | apples are a tasty treat
0.5285 | induces root tip closure in non-vital teeth
0.5110 | a brick left by Yuki
0.5044 | the weed whacker uses an engine that runs on a mixture of gas and oil
0.4734 | a plant hormone for regulating stress responses
0.4162 | a type of cancer treatment that uses drugs to boost the body's immune response

Train the model

Now let's train our model. We use MultipleNegativesRankingLoss

This loss function uses other positives in the same batch as negative examples, which is efficient for contrastive learning.

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Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]
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GPU = Tesla T4. Max memory = 14.741 GB.
1.256 GB of memory reserved.

Let's train the model! To resume a training run, set trainer.train(resume_from_checkpoint = True)

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==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
   \\   /|    Num examples = 106,628 | Num Epochs = 2 | Total steps = 834
O^O/ \_/ \    Batch size per device = 256 | Gradient accumulation steps = 1
\        /    Data Parallel GPUs = 1 | Total batch size (256 x 1 x 1) = 256
 "-____-"     Trainable parameters = 20,185,088 of 615,961,600 (3.28% trained)
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916.4403 seconds used for training.
15.27 minutes used for training.
Peak reserved memory = 13.932 GB.
Peak reserved memory for training = 12.676 GB.
Peak reserved memory % of max memory = 94.512 %.
Peak reserved memory for training % of max memory = 85.991 %.

Inference

Let's run the model after training to see the improvements!

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--- Post-Training Results for query: 'apexification' ---
0.2650 | induces root tip closure in non-vital teeth
0.1967 | a type of cancer treatment that uses drugs to boost the body's immune response
0.0913 | the weed whacker uses an engine that runs on a mixture of gas and oil
0.0846 | apples are a tasty treat
0.0555 | a plant hormone for regulating stress responses
0.0027 | a brick left by Yuki

Saving, loading finetuned models

To save the final model as LoRA adapters, either use Huggingface's push_to_hub for an online save or save_pretrained for a local save.

[NOTE] This ONLY saves the LoRA adapters, and not the full model. To save to 16bit, scroll down!

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Now if you want to load the LoRA adapters we just saved for inference, set False to True:

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Saving to float16 for vLLM

We also support saving to float16 directly. Select merged_16bit for float16 or merged_4bit for int4. We also allow lora adapters as a fallback. Use push_to_hub_merged to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See our docs for more deployment options.

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GGUF / llama.cpp Conversion

To save to GGUF / llama.cpp, we support it natively now! We clone llama.cpp and we default save it to q8_0. We allow all methods like q4_k_m. Use save_pretrained_gguf for local saving and push_to_hub_gguf for uploading to HF.

Some supported quant methods (full list on our Wiki page):

  • q8_0 - Fast conversion. High resource use, but generally acceptable.
  • q4_k_m - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.
  • q5_k_m - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
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