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@@ -9,16 +9,16 @@ license: apache-2.0
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  language:
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  - en
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  datasets:
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- - Tesslate/Tessa-Rust_Dataset
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  ---
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- ## **Tessa-Rust, A Rust Focused Code Generation Model**
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/c-1bYQHPxRKXxQdPPKKOZ.png)
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  ## **Model Overview**
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- Tessa-Rust is a transformer-based **Rust code generation model**, fine-tuned from the powerful **Qwen2.5-Coder-7B-Instruct** base model. Designed specifically for Rust development, Tessa-Rust leverages advanced reasoning to autonomously generate well-structured, idiomatic Rust code, including functions, structs, traits, and modules. Its integration into agent systems makes it a powerful tool for automating backend development, systems programming, CLI tool creation, and Rust code intelligence.
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  ---
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@@ -61,7 +61,7 @@ Tessa-Rust is a transformer-based **Rust code generation model**, fine-tuned fro
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Make sure to use the correct model name you decide on
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- model_name = "tesslate/Tessa-Rust" # Adjusted hypothetical name
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") # Assumes CUDA availability
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@@ -111,8 +111,8 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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  ## **Citation**
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  ```bibtex
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- @misc{tesslate_Tessa-Rust, # Adjusted name
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- title={Tessa-Rust: A Rust-Focused Code Generation Model},
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  author={tesslate},
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  year={2025}, # Placeholder year
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  publisher={Hugging Face},
 
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  language:
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  - en
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  datasets:
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+ - Tesslate/Tessa-Rust-T1_Dataset
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  ---
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+ ## **Tessa-Rust-T1, A Rust Focused Code Generation Model**
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  ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64d1129297ca59bcf7458d07/c-1bYQHPxRKXxQdPPKKOZ.png)
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  ## **Model Overview**
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+ Tessa-Rust-T1 is a transformer-based **Rust code generation model**, fine-tuned from the powerful **Qwen2.5-Coder-7B-Instruct** base model. Designed specifically for Rust development, Tessa-Rust-T1 leverages advanced reasoning to autonomously generate well-structured, idiomatic Rust code, including functions, structs, traits, and modules. Its integration into agent systems makes it a powerful tool for automating backend development, systems programming, CLI tool creation, and Rust code intelligence.
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  ---
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  # Make sure to use the correct model name you decide on
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+ model_name = "tesslate/Tessa-Rust-T1" # Adjusted hypothetical name
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") # Assumes CUDA availability
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  ## **Citation**
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  ```bibtex
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+ @misc{tesslate_Tessa-Rust-T1, # Adjusted name
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+ title={Tessa-Rust-T1: A Rust-Focused Code Generation Model},
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  author={tesslate},
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  year={2025}, # Placeholder year
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  publisher={Hugging Face},