# MyModel_xh by Bi: Open-Weight Model Source: https://savrn.com/models/mymodel-xh Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve MyModel_xh (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](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 7.6 GB | 9.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 3.8 GB | 4.6 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/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](https://savrn.com/ai-index/pricing/gpus), read Oct 12, 2026. [MyModel_xh on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/mymodel-xh/gpus) ## Model Card By Bi, published under apache-2.0, revision 4cd7528419bf. This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library. Read Bi's full model card ### Uploaded finetuned model - Developed by: zxcd1 - License: apache-2.0 - Finetuned from model : unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. ## Configuration Architecture Qwen2ForCausalLM Context length (tokens) 131,072 Layers 28 Hidden size 3,584 Feed-forward size 18,944 Attention heads 28 Key/value heads 4 Vocabulary size 152,064 Model type qwen2 ## Identity and Version Repository zxcd1/MyModel_xh Publisher Bi Task Text generation Modality Text Library transformers Parameters 7.6B parameters Languages en Revision 4cd7528419bf8c5a09404e5e96c5f458586124e7 First published 2026-09-08 Last updated 2026-10-10 ## Files and Weights 10 files, 15.7 GB in total. The weights are 1 file totalling 15.2 GB in safetensors. Weights1 file · 15.2 GB Configuration3 files · 2.0 KB Tokenizer2 files · 11.4 MB Documentation1 file · 630 B Other2 files · 450.0 MB Repository1 file · 1.6 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 15.2 GB | f81452e1ab2a | | config.json | Configuration | 1.4 KB | — | | generation_config.json | Configuration | 181 B | — | | special_tokens_map.json | Configuration | 472 B | — | | README.md | Documentation | 630 B | — | | chat_template.jinja | Other | 2.2 KB | — | | lora_adapter.zip | Other | 450.0 MB | 97eeb79f65b7 | | .gitattributes | Repository | 1.6 KB | — | | tokenizer.json | Tokenizer | 11.4 MB | f624f8136bc5 | | tokenizer_config.json | Tokenizer | 421 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 15.2 GB [Download from Bi](https://huggingface.co/zxcd1/MyModel_xh) Released by Bi through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit ## 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 MyModel_xh ### How much GPU memory does MyModel_xh 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 MyModel_xh 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 MyModel_xh commercially? Yes. MyModel_xh 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. ### What is MyModel_xh's context length? 131,072 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [Qwen2.5-7B-Instruct](https://savrn.com/models/qwen2-5-7b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and… Open weights apache-2.0 7.6B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-7b-instruct) Model · Text generation ### [Qwen2.5-7B-Instruct-AWQ](https://savrn.com/models/qwen2-5-7b-instruct-awq) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and… Open weights apache-2.0 7.6B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-7b-instruct-awq) Model · Text generation ### [Qwen2.5-Coder-7B-Instruct](https://savrn.com/models/qwen2-5-coder-7b-instruct) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more… Open weights apache-2.0 7.6B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-coder-7b-instruct) Model · Text generation ### [Qwen2.5-Coder-7B-Instruct-AWQ](https://savrn.com/models/qwen2-5-coder-7b-instruct-awq) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more… Open weights apache-2.0 7.6B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-coder-7b-instruct-awq) Model · Text generation ### [Qwen2.5-Coder-7B](https://savrn.com/models/qwen2-5-coder-7b) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more… Open weights apache-2.0 7.6B parameters 32,768 tokens transformers [View model](https://savrn.com/models/qwen2-5-coder-7b) Model · Text generation ### [Qwen2.5-7B](https://savrn.com/models/qwen2-5-7b) [Qwen](https://savrn.com/model-publishers/qwen) Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains. - Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and… Open weights apache-2.0 7.6B parameters 131,072 tokens transformers [View model](https://savrn.com/models/qwen2-5-7b) ## Bi [All models and datasets](https://savrn.com/model-publishers/zxcd1) ## Versions - [4cd7528419bf](https://savrn.com/models/mymodel-xh/versions/4cd7528419bf) · current 2026-10-10 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-10. - [Hugging Face record](https://huggingface.co/zxcd1/MyModel_xh) - [How the hub is built](https://savrn.com/model-hub/methodology)