Update README.md
Browse files
README.md
CHANGED
@@ -9,16 +9,16 @@ license: apache-2.0
|
|
9 |
language:
|
10 |
- en
|
11 |
datasets:
|
12 |
-
- Tesslate/Tessa-
|
13 |
---
|
14 |
|
15 |
-
## **Tessa-Rust, A Rust Focused Code Generation Model**
|
16 |
|
17 |

|
18 |
|
19 |
## **Model Overview**
|
20 |
|
21 |
-
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.
|
22 |
|
23 |
---
|
24 |
|
@@ -61,7 +61,7 @@ Tessa-Rust is a transformer-based **Rust code generation model**, fine-tuned fro
|
|
61 |
from transformers import AutoModelForCausalLM, AutoTokenizer
|
62 |
|
63 |
# Make sure to use the correct model name you decide on
|
64 |
-
model_name = "tesslate/Tessa-Rust" # Adjusted hypothetical name
|
65 |
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
66 |
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") # Assumes CUDA availability
|
67 |
|
@@ -111,8 +111,8 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
111 |
## **Citation**
|
112 |
|
113 |
```bibtex
|
114 |
-
@misc{tesslate_Tessa-Rust, # Adjusted name
|
115 |
-
title={Tessa-Rust: A Rust-Focused Code Generation Model},
|
116 |
author={tesslate},
|
117 |
year={2025}, # Placeholder year
|
118 |
publisher={Hugging Face},
|
|
|
9 |
language:
|
10 |
- en
|
11 |
datasets:
|
12 |
+
- Tesslate/Tessa-Rust-T1_Dataset
|
13 |
---
|
14 |
|
15 |
+
## **Tessa-Rust-T1, A Rust Focused Code Generation Model**
|
16 |
|
17 |

|
18 |
|
19 |
## **Model Overview**
|
20 |
|
21 |
+
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.
|
22 |
|
23 |
---
|
24 |
|
|
|
61 |
from transformers import AutoModelForCausalLM, AutoTokenizer
|
62 |
|
63 |
# Make sure to use the correct model name you decide on
|
64 |
+
model_name = "tesslate/Tessa-Rust-T1" # Adjusted hypothetical name
|
65 |
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
66 |
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda") # Assumes CUDA availability
|
67 |
|
|
|
111 |
## **Citation**
|
112 |
|
113 |
```bibtex
|
114 |
+
@misc{tesslate_Tessa-Rust-T1, # Adjusted name
|
115 |
+
title={Tessa-Rust-T1: A Rust-Focused Code Generation Model},
|
116 |
author={tesslate},
|
117 |
year={2025}, # Placeholder year
|
118 |
publisher={Hugging Face},
|