Instructions to use dipeshmajithia/MirrorCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipeshmajithia/MirrorCode with Transformers:

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

pipe = pipeline("text-generation", model="dipeshmajithia/MirrorCode")
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("dipeshmajithia/MirrorCode")
model = AutoModelForCausalLM.from_pretrained("dipeshmajithia/MirrorCode", 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 dipeshmajithia/MirrorCode with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dipeshmajithia/MirrorCode

SGLang

How to use dipeshmajithia/MirrorCode 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 "dipeshmajithia/MirrorCode" \
    --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": "dipeshmajithia/MirrorCode",
		"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 "dipeshmajithia/MirrorCode" \
        --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": "dipeshmajithia/MirrorCode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dipeshmajithia/MirrorCode with Docker Model Runner:
```
docker model run hf.co/dipeshmajithia/MirrorCode
```

dipeshmajithia commited on Mar 17, 2025

Commit

2e6d4bf

verified ·

1 Parent(s): a0261a3

Update README.md

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README.md +37 -27

README.md CHANGED Viewed

@@ -59,46 +59,56 @@ hf_pipeline = HuggingFacePipeline(pipeline=generate_code)
 llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
 print(llm_chain.predict(instruction="Write a Python function to check if a number is prime."))
-Known Limitations
 While Mirror provides high-quality code suggestions, debugging assistance, and structured programming responses, it has the following limitations:
-General conversation abilities are limited due to its specialization in coding-related tasks.
-Mathematical reasoning and logical inference may be weaker than models designed for general problem-solving.
-Complex multi-step reasoning in natural language might require fine-tuning on additional dialogue datasets.
-Dataset Limitations
-Mirror is fine-tuned on the GPT CodeFeedback dataset, which primarily focuses on code optimization and structured feedback. While it provides strong performance for technical queries, it may:
-Reflect biases inherent in publicly available programming datasets.
-Have limited knowledge of recent programming frameworks or libraries that emerged after its last fine-tuning session.
-Exhibit hallucinations in open-ended prompts that lack specific instructions.
-Future Development
-Enhancing conversational abilities by fine-tuning on instruction-heavy dialogue datasets (e.g., OpenAssistant, Dolly).
-Improving reasoning and debugging capabilities using reinforcement learning from developer interactions.
-Reducing hallucinations in long-form responses through dataset refinements.
-License
-Mirror is released under the Apache License 2.0 and CC-BY-SA 4.0, allowing for both commercial and research usage.
-Option 1: Apache License 2.0
-Mirror is licensed under the Apache License, Version 2.0 (the "License");
-you may not use this model except in compliance with the License.
 You may obtain a copy of the License at:
-📄 Apache 2.0 License
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-Option 2: Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA 4.0)
-This model's outputs (such as generated text) and non-code content are licensed under CC-BY-SA 4.0.
 Under this license:
-You must give credit when using or sharing outputs.
-You must share modifications under the same license.
-📄 CC-BY-SA 4.0 License

 llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
 print(llm_chain.predict(instruction="Write a Python function to check if a number is prime."))
+```
+## **Known Limitations**
 While Mirror provides high-quality code suggestions, debugging assistance, and structured programming responses, it has the following limitations:
+- **General conversation abilities** are limited due to its specialization in coding-related tasks.
+- **Mathematical reasoning and logical inference** may be weaker than models designed for general problem-solving.
+- **Complex multi-step reasoning** in natural language might require fine-tuning on additional dialogue datasets.
+---
+## **Dataset Limitations**
+Mirror is fine-tuned on the **GPT CodeFeedback dataset**, which primarily focuses on **code optimization and structured feedback**. While it provides strong performance for technical queries, it may:
+- Reflect biases inherent in **publicly available programming datasets**.
+- Have **limited knowledge of recent programming frameworks or libraries** that emerged after its last fine-tuning session.
+- Exhibit **hallucinations** in open-ended prompts that lack specific instructions.
+---
+## **Future Development**
+- **Enhancing conversational abilities** by fine-tuning on instruction-heavy dialogue datasets (e.g., OpenAssistant, Dolly).
+- **Improving reasoning and debugging capabilities** using reinforcement learning from developer interactions.
+- **Reducing hallucinations in long-form responses** through dataset refinements.
+---
+## **License**
+Mirror is released under the **Apache License 2.0** and **CC-BY-SA 4.0**, allowing for both **commercial and research usage**.
+### **Option 1: Apache License 2.0**
+Mirror is licensed under the **Apache License, Version 2.0** (the "License");
+you may not use this model except in compliance with the License.
 You may obtain a copy of the License at:
+📄 **[Apache 2.0 License](http://www.apache.org/licenses/LICENSE-2.0)**
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+### **Option 2: Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA 4.0)**
+This model's outputs (such as generated text) and non-code content are licensed under **CC-BY-SA 4.0**.
 Under this license:
+- You **must give credit** when using or sharing outputs.
+- You **must share modifications under the same license**.
+📄 **[CC-BY-SA 4.0 License](https://creativecommons.org/licenses/by-sa/4.0/)**