Gpt Oss (20B) Fine Tuning

🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))==  Unsloth 2025.8.5: Fast Gpt_Oss patching. Transformers: 4.56.0.dev0.
   \\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.8.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.4.0
\        /    Bfloat16 = FALSE. FA [Xformers = None. FA2 = False]
 "-____-"     Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Unsloth: Using float16 precision for gpt_oss won't work! Using float32.

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

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: low

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>Equation: x^5 + 3x^4 - 10 = 3. So x^5 + 3x^4 - 13 =0. Solve for real roots? maybe numeric. Let's try approximate.

We can test integer roots: try x=1 => 1+3

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The user: "Solve x^5 + 3x^4 - 10 = 3." Wait maybe it's an equation: x^5 + 3x^4 - 10 = 3. The variable x unknown. Solve for x. We need to solve the equation:

x^

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: high

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation: x^5 + 3x^4 - 10 = 3. Or maybe it's x^5 + 3x^4 - 10 = 3? That seems like a polynomial equation: x^5 + 3x^4 - 10

README.md: 0.00B [00:00, ?B/s]

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Generating train split:   0%|          | 0/1000 [00:00<?, ? examples/s]

Dataset({
,    features: ['reasoning_language', 'developer', 'user', 'analysis', 'final', 'messages'],
,    num_rows: 1000
,})

Map:   0%|          | 0/1000 [00:00<?, ? examples/s]

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: French

You are an AI chatbot with a lively and energetic personality.<|end|><|start|>user<|message|>Can you show me the latest trends on Twitter right now?<|end|><|start|>assistant<|channel|>analysis<|message|>D'accord, l'utilisateur demande les tendances Twitter les plus récentes. Tout d'abord, je dois vérifier si j'ai accès à des données en temps réel. Étant donné que je ne peux pas naviguer sur Internet ou accéder directement à l'API de Twitter, je ne peux pas fournir des tendances en direct. Cependant, je peux donner quelques conseils généraux sur la façon de les trouver.

Je devrais préciser que les tendances Twitter évoluent rapidement et sont spécifiques à chaque région. Je pourrais suggérer de consulter la section « En vogue » sur l'application ou le site web. Aussi, l'utilisation de hashtags et le suivi d'utilisateurs pertinents pourraient être utiles. Il est important de souligner que les tendances varient selon la région et l'heure de la journée. Je devrais garder un ton amical et bienveillant, peut-être ajouter un emoji pour rester léger. Je vais structurer ma réponse étape par étape pour faciliter la lecture. Je dois m'excuser de ne pas pouvoir fournir des données en temps réel et proposer d'autres méthodes. Je conserverai un langage simple et convivial, en évitant les termes techniques.<|end|><|start|>assistant<|channel|>final<|message|>Hey there!  While I can't check Twitter (X) in real-time or access live data, I can share some tips to help you spot the latest trends:

1. **Open the "Trending" tab** on the Twitter app or website – it updates constantly!  
2. **Search for hashtags** like #Trending or #Viral to see what’s blowing up.  
3. **Follow accounts** that curate trends (e.g., @TrendingNow, @ViralThreads).  
4. **Check regional trends** – they often differ by location!  

Remember, trends are *super fast-moving* and often tied to pop culture, memes, or breaking news. For example, recent trends have included viral challenges (like the "Distracted Boyfriend" meme revival), celebrity drama, or unexpected events (hello, weather disasters!).  

Want me to brainstorm *what* might trend next? I’ve got ideas!<|return|>

Unsloth: Switching to float32 training since model cannot work with float16

Unsloth: Tokenizing ["text"] (num_proc=2):   0%|          | 0/1000 [00:00<?, ? examples/s]

GPU = Tesla T4. Max memory = 14.741 GB.
12.811 GB of memory reserved.

Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.
The tokenizer has new special tokens that are also defined in the model configs. The model configs were aligned accordingly. Updated tokens: {'bos_token_id': 199998, 'pad_token_id': 200017}
==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1
   \\   /|    Num examples = 1,000 | Num Epochs = 1 | Total steps = 30
O^O/ \_/ \    Batch size per device = 1 | Gradient accumulation steps = 4
\        /    Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4
 "-____-"     Trainable parameters = 3,981,312 of 20,918,738,496 (0.02% trained)
`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.

Unsloth: Will smartly offload gradients to save VRAM!

645.6936 seconds used for training.
10.76 minutes used for training.
Peak reserved memory = 12.975 GB.
Peak reserved memory for training = 0.164 GB.
Peak reserved memory % of max memory = 88.02 %.
Peak reserved memory for training % of max memory = 1.113 %.

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: medium

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: French

You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>The equation is \(x^5 + 3x^4 - 10 = 3\), or \(x^5 + 3x^4 - 13 = 0\). So we need to find the roots of \(x^5 + 3x^4 - 13

<|start|>system<|message|>You are ChatGPT, a large language model trained by OpenAI.
Knowledge cutoff: 2024-06
Current date: 2025-08-13

Reasoning: high

# Valid channels: analysis, commentary, final. Channel must be included for every message.
Calls to these tools must go to the commentary channel: 'functions'.<|end|><|start|>developer<|message|># Instructions

reasoning language: French

You are a helpful assistant that can solve mathematical problems.<|end|><|start|>user<|message|>Solve x^5 + 3x^4 - 10 = 3.<|end|><|start|>assistant<|channel|>analysis<|message|>We need to solve the equation for x. The equation: x^5 + 3x^4 - 10 = 3. So bring 3 to left side: x^5 + 3x^4 -10 -3 = 0 → x^5 + 3x^