Open-weight model
tourn-05266233-instructtext-super-v4pre
by Firza Hadzami firzahdzm/tourn-05266233-instructtext-super-v4pre
tourn-05266233-instructtext-super-v4pre is an open-weight model from Firza Hadzami. It has 1.4B parameters and a 2,048-token context. At 16-bit it needs about 3.4 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Runs On
What it takes to serve tourn-05266233-instructtext-super-v4pre (1.4B 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 | 2.8 GB | 3.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 1.4 GB | 1.7 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.7 GB | 0.9 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 18, 2026.
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- PhiForCausalLM
- Context length (tokens)
- 2,048
- Layers
- 24
- Hidden size
- 2,048
- Feed-forward size
- 8,192
- Attention heads
- 32
- Key/value heads
- 32
- Vocabulary size
- 51,200
- RoPE base
- 10000
- Stored precision
- bfloat16
- Model type
- phi
Identity and Version
- Repository
- firzahdzm/tourn-05266233-instructtext-super-v4pre
- Publisher
- Firza Hadzami
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 1.4B parameters
- Languages
- phi
- Revision
- e7dbd8de30d01aaa01433082acfa4f33a848efbc
- First published
- 2026-09-25
- Last updated
- 2026-09-25
Files and Weights
11 files, 2.8 GB in total. The weights are 1 file totalling 2.8 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 2.8 GB | 6028ddbc6234 |
| added_tokens.json | Configuration | 1.1 KB | — |
| config.json | Configuration | 696 B | — |
| generation_config.json | Configuration | 69 B | — |
| special_tokens_map.json | Configuration | 473 B | — |
| loss.txt | Other | 23 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| merges.txt | Tokenizer | 456.3 KB | — |
| tokenizer.json | Tokenizer | 3.6 MB | — |
| tokenizer_config.json | Tokenizer | 7.4 KB | — |
| vocab.json | Tokenizer | 798.2 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 2.8 GB
Released by Firza Hadzami through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 2.8 GB |
| 16-bit | 2.8 GB |
| 8-bit | 1.4 GB |
| 4-bit | 0.7 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About tourn-05266233-instructtext-super-v4pre
How much GPU memory does tourn-05266233-instructtext-super-v4pre need?
About 3.4 GB at 16-bit and 0.9 GB at 4-bit: the weights (1.4B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run tourn-05266233-instructtext-super-v4pre 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.
What is tourn-05266233-instructtext-super-v4pre's context length?
2,048 tokens, from the maximum position embeddings in its published configuration.