This model is a fine-tuned version of distilbert/distilgpt2 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3.0 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2
rick-morty-distilgpt2 is an open-weight model for text generation from Zune toka, released under Apache License 2.0. It has 82M parameters. At 16-bit it needs about 0.2 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 4 - evalbatchsize: 4 …
Parameters82M
Context—
Weights327.7 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve rick-morty-distilgpt2 (82M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.2 GB | 0.2 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 7, 2026.
rick-morty-distilgpt2 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Zune toka, published under apache-2.0, revision 69b63b02c780.
This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 4 - evalbatchsize: 4 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 16 - lrschedulertype: linear - numepochs: 1 - mixedprecisiontraining: Native AMP - Transformers 5.17.0 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1
Read Zune toka's full model card
This model is a fine-tuned version of distilbert/distilgpt2 on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0127
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 3 | 3.0127 |
Framework versions
- Transformers 5.17.0
- Pytorch 2.11.0+cu128
- Datasets 5.0.1
- Tokenizers 0.23.1
Configuration
- Architecture
- GPT2LMHeadModel
- Vocabulary size
- 50,257
- Model type
- gpt2
Identity and Version
- Repository
- Hfzune/rick-morty-distilgpt2
- Publisher
- Zune toka
- Task
- Text generation
- Modality
- Text
- Library
- transformers
- Parameters
- 82M parameters
- Languages
- Not stated by the source
- Revision
- 69b63b02c78045227872010adaac91f0b9320dc1
- First published
- 2026-09-27
- Last updated
- 2026-09-27
Files and Weights
8 files, 331.2 MB in total. The weights are 2 files totalling 327.7 MB in bin, safetensors.
Weights2 files · 327.7 MB
Configuration2 files · 1.2 KB
Tokenizer2 files · 3.6 MB
Documentation1 file · 1.5 KB
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 327.7 MB | fe72a5cc7849 |
| training_args.bin | Weights | 5.3 KB | db6556200af5 |
| config.json | Configuration | 1.1 KB | — |
| generation_config.json | Configuration | 154 B | — |
| README.md | Documentation | 1.5 KB | — |
| .gitattributes | Repository | 1.5 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 326 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 327.7 MB
Download from Zune toka
Released by Zune toka through its official repository on Hugging Face. Read the license.
Built From
- Derived from distilbert/distilgpt2
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 327.7 MB |
| 16-bit | 0.2 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About rick-morty-distilgpt2
How much GPU memory does rick-morty-distilgpt2 need?
About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (82M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run rick-morty-distilgpt2 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use rick-morty-distilgpt2 commercially?
Yes. rick-morty-distilgpt2 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
Similar Models
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1
This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1
Modelo de lenguaje entrenado desde cero (pesos aleatorios) por Victor Cherif. Arquitectura tipo Llama de ~110M de parametros, pensado como asistente en espanol orientado a ciencia, programacion, robotica y fisica, con soporte de tool-calling / MCP entrenado contra servidores MCP reales. RMSNorm + SwiGLU - Al ser un modelo pequeno (~110M), el lenguaje puede ser incoherente en respuestas largas, sobre todo pasados los primeros parrafos (tiende a repetir bloques o rellenar con generalidades). - La aritmetica mental sigue sin ser confiable: depende de que el modelo dispare la tool calculate en vez de calcular "de memoria" -- verificado que esto mejoro pero no es perfecto. - El razonamiento ( )…
cRia-LM-75M is a 75.7M-parameter base language model built as a Relaxed Recursive Transformer (RRT). It uses a shared 11-layer recurrent block evaluated twice, with pass-specific LoRA parameters on the second traversal. Training was carried out in three stages. Stage 1 established the 2K base model over 10B tokens. Stage 2 continued training with a 2B-token budget and a capability-focused data curriculum; the released Stage 2 checkpoint is step 10,000, corresponding to about 1.31B continuation tokens. Stage 3 extended the context window from 2,048 to 4,096 tokens with a 50M-token run on codelion/sutra-1B. The released checkpoint continues from that long-context stage with 35 learned…