Instructions to use Anant58/swe-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anant58/swe-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Anant58/swe-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Anant58/swe-model")
model = AutoModelForCausalLM.from_pretrained("Anant58/swe-model", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anant58/swe-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anant58/swe-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anant58/swe-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anant58/swe-model

SGLang

How to use Anant58/swe-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Anant58/swe-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anant58/swe-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Anant58/swe-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anant58/swe-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anant58/swe-model with Docker Model Runner:
```
docker model run hf.co/Anant58/swe-model
```

Anant58 commited on Mar 28, 2025

Commit

29eb6e4

verified ·

1 Parent(s): 9889cb7

Update README.md

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README.md +10 -21

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ base_model:
 ## Model Overview
-This model has been fine-tuned on the `swe_bench` dataset to automate the generation of bug fixes in software engineering tasks. It leverages issue descriptions, code diffs, and historical bug context to generate precise patches. The primary use case is to assist developers by quickly generating code fixes based on detailed bug descriptions.
 ### Key Features:
 - **Patch Generation**: Produces code patches based on issue descriptions and optional context.
@@ -23,11 +23,11 @@ This model has been fine-tuned on the `swe_bench` dataset to automate the genera
 The model is designed for developers and software teams to automatically generate code patches for software issues. It can handle a variety of inputs such as issue descriptions and additional context and is ideal for teams dealing with frequent bug reports.
 ### Inputs:
-1. **Issue Description (<issue>)**: Main input, taken from `fix_issue_description`.
-2. **Issue Story (<fix_issue_story>)**: Optional additional context, from `fix_story`.
-3. **Assertions (<assertions>)**: Conditions that the patch must meet, from `fix_assertion_1`, `fix_assertion_2`, etc.
-4. **Bug and PR Context (<bug>)**: Historical bug and PR context from fields like `bug_pr`, `bug_story`, etc., used during fine-tuning but not required for inference.
-5. **File Paths and Code Differences (<file>, <bug_code_diff>, <fix_code_diff>)**: Paths and diffs used to generate a valid code patch.
 ### Outputs:
 - **Generated Code Patch**: Based on input issue description and additional context.
@@ -35,7 +35,8 @@ The model is designed for developers and software teams to automatically generat
 ## Dataset
-### Dataset: `swe_bench-lite`
 This model is fine-tuned on the `swe_bench` dataset. The dataset includes:
 - **<issue> (Issue Description)**: Describes the bug in detail.
 - **<fix_issue_story> (Issue Story)**: Provides additional narrative or context around the bug.
@@ -43,6 +44,7 @@ This model is fine-tuned on the `swe_bench` dataset. The dataset includes:
 - **<bug> (Bug and PR Context)**: Provides historical context on bugs, used in fine-tuning.
 - **<file>, <bug_code_diff>, <fix_code_diff>**: File paths and code diffs used to train the model to generate fixes.
 ## Limitations
 - The model relies on well-defined issue descriptions to produce accurate patches.
@@ -69,17 +71,4 @@ print(tokenizer.decode(outputs[0]))
 ## Ethical Considerations
 - The generated patches should always be validated and reviewed by developers before deploying in production.
-- This model is designed to assist but should not replace thorough code reviews.
-## Citation
-Please cite this model as follows:
-```
-@inproceedings{tandon_ft_llama3_1_swe_bench,
-  title={Automatic Bug Fix Generation using Llama-3.1 and SWE Bench Dataset},
-  author={Gauransh Tandon, Caroline Lemiuex, and Reid Holmes},
-  year={2024},
-  publisher={Hugging Face}
-}
-```

 ## Model Overview
+This model has been fine-tuned on the `princeton-nlp/SWE-bench_Lite` dataset to automate the generation of bug fixes in software engineering tasks. It leverages issue descriptions, code diffs, and historical bug context to generate precise patches. The primary use case is to assist developers by quickly generating code fixes based on detailed bug descriptions.
 ### Key Features:
 - **Patch Generation**: Produces code patches based on issue descriptions and optional context.
 The model is designed for developers and software teams to automatically generate code patches for software issues. It can handle a variety of inputs such as issue descriptions and additional context and is ideal for teams dealing with frequent bug reports.
 ### Inputs:
+1. **Issue Description (`<issue>`)**: Main input, taken from `fix_issue_description`.
+2. **Issue Story (`<fix_issue_story>`)**: Optional additional context, from `fix_story`.
+3. **Assertions (`<assertions>`)**: Conditions that the patch must meet, from `fix_assertion_1`, `fix_assertion_2`, etc.
+4. **Bug and PR Context (`<bug>`)**: Historical bug and PR context from fields like `bug_pr`, `bug_story`, etc., used during fine-tuning but not required for inference.
+5. **File Paths and Code Differences (`<file>`, `<bug_code_diff>`, `<fix_code_diff>`)**: Paths and diffs used to generate a valid code patch.
 ### Outputs:
 - **Generated Code Patch**: Based on input issue description and additional context.
 ## Dataset
+### Dataset: `princeton-nlp/SWE-bench_Lite`
 This model is fine-tuned on the `swe_bench` dataset. The dataset includes:
 - **<issue> (Issue Description)**: Describes the bug in detail.
 - **<fix_issue_story> (Issue Story)**: Provides additional narrative or context around the bug.
 - **<bug> (Bug and PR Context)**: Provides historical context on bugs, used in fine-tuning.
 - **<file>, <bug_code_diff>, <fix_code_diff>**: File paths and code diffs used to train the model to generate fixes.
 ## Limitations
 - The model relies on well-defined issue descriptions to produce accurate patches.
 ## Ethical Considerations
 - The generated patches should always be validated and reviewed by developers before deploying in production.
+- This model is designed to assist but should not replace thorough code reviews.