Open-weight model
qwen25-coder-7b-func-localize-claude45-1457i-text0
by Synthetic Code Training synthetic-code-training/qwen25-coder-7b-func-localize-claude45-1457i-text0
qwen25-coder-7b-func-localize-claude45-1457i-text0 is an open-weight model from Synthetic Code Training. It has 7.6B parameters and a 32,768-token context. At 16-bit it needs about 18.3 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 qwen25-coder-7b-func-localize-claude45-1457i-text0 (7.6B 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 | 15.2 GB | 18.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 7.6 GB | 9.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 3.8 GB | 4.6 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
- Qwen2ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 28
- Hidden size
- 3,584
- Feed-forward size
- 18,944
- Attention heads
- 28
- Key/value heads
- 4
- Vocabulary size
- 152,064
- Sliding window (tokens)
- 131,072
- RoPE base
- 1e+06
- Stored precision
- bfloat16
- Model type
- qwen2
Identity and Version
- Repository
- synthetic-code-training/qwen25-coder-7b-func-localize-claude45-1457i-text0
- Publisher
- Synthetic Code Training
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 7.6B parameters
- Languages
- Not stated by the source
- Revision
- 9a466646b457584faf399263b0df2f77175ed9a8
- First published
- 2026-09-22
- Last updated
- 2026-09-22
Files and Weights
14 files, 15.2 GB in total. The weights are 4 files totalling 15.2 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00004.safetensors | Weights | 4.9 GB | df35afbe251a |
| model-00002-of-00004.safetensors | Weights | 4.9 GB | c116e91f12d9 |
| model-00003-of-00004.safetensors | Weights | 4.3 GB | dfa5471d3d73 |
| model-00004-of-00004.safetensors | Weights | 1.1 GB | fb26bf992083 |
| added_tokens.json | Configuration | 605 B | — |
| config.json | Configuration | 687 B | — |
| generation_config.json | Configuration | 242 B | — |
| model.safetensors.index.json | Configuration | 27.8 KB | — |
| special_tokens_map.json | Configuration | 613 B | — |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 11.4 MB | 9c5ae00e602b |
| tokenizer_config.json | Tokenizer | 7.3 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 15.2 GB
Released by Synthetic Code Training through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 15.2 GB |
| 16-bit | 15.2 GB |
| 8-bit | 7.6 GB |
| 4-bit | 3.8 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About qwen25-coder-7b-func-localize-claude45-1457i-text0
How much GPU memory does qwen25-coder-7b-func-localize-claude45-1457i-text0 need?
About 18.3 GB at 16-bit and 4.6 GB at 4-bit: the weights (7.6B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run qwen25-coder-7b-func-localize-claude45-1457i-text0 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 qwen25-coder-7b-func-localize-claude45-1457i-text0's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.