Open-weight model
qwen3-1.7b-sft-all-first200-step100
by Jingwen Gu JingwenGu/qwen3-1.7b-sft-all-first200-step100
qwen3-1.7b-sft-all-first200-step100 is an open-weight model from Jingwen Gu. It has 2B parameters and a 32,768-token context. At 16-bit it needs about 4.9 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 qwen3-1.7b-sft-all-first200-step100 (2B 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 | 4.1 GB | 4.9 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 2.0 GB | 2.4 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 1.0 GB | 1.2 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 19, 2026.
qwen3-1.7b-sft-all-first200-step100 on every accelerator the SAVRN Index prices, at every precision
Model Card
The publisher has not written a card for this model.
Configuration
- Architecture
- Qwen3ForCausalLM
- Context length (tokens)
- 32,768
- Layers
- 28
- Hidden size
- 2,048
- Feed-forward size
- 6,144
- Attention heads
- 16
- Key/value heads
- 8
- Head dimension
- 128
- Vocabulary size
- 151,936
- RoPE base
- 1,000,000
- Stored precision
- bfloat16
- Model type
- qwen3
Identity and Version
- Repository
- JingwenGu/qwen3-1.7b-sft-all-first200-step100
- Publisher
- Jingwen Gu
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 2B parameters
- Languages
- Not stated by the source
- Revision
- 4d25645d40c3cc7f9626d150c91d02817bafd51b
- First published
- 2026-10-13
- Last updated
- 2026-10-13
Files and Weights
10 files, 4.1 GB in total. The weights are 1 file totalling 4.1 GB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 4.1 GB | 9a2f4bb4a187 |
| added_tokens.json | Configuration | 707 B | — |
| config.json | Configuration | 1.4 KB | — |
| generation_config.json | Configuration | 117 B | — |
| special_tokens_map.json | Configuration | 616 B | — |
| .gitattributes | Repository | 1.6 KB | — |
| merges.txt | Tokenizer | 1.7 MB | — |
| tokenizer.json | Tokenizer | 11.4 MB | aeb13307a71a |
| tokenizer_config.json | Tokenizer | 9.7 KB | — |
| vocab.json | Tokenizer | 2.8 MB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 4.1 GB
Released by Jingwen Gu through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 4.1 GB |
| 16-bit | 4.1 GB |
| 8-bit | 2.0 GB |
| 4-bit | 1.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About qwen3-1.7b-sft-all-first200-step100
How much GPU memory does qwen3-1.7b-sft-all-first200-step100 need?
About 4.9 GB at 16-bit and 1.2 GB at 4-bit: the weights (2B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run qwen3-1.7b-sft-all-first200-step100 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 qwen3-1.7b-sft-all-first200-step100's context length?
32,768 tokens, from the maximum position embeddings in its published configuration.