Instructions to use Agasthya0/colabmind-coder-6.7b-ml-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agasthya0/colabmind-coder-6.7b-ml-qlora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-base")
model = PeftModel.from_pretrained(base_model, "Agasthya0/colabmind-coder-6.7b-ml-qlora")

Transformers

How to use Agasthya0/colabmind-coder-6.7b-ml-qlora with Transformers:

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

pipe = pipeline("text-generation", model="Agasthya0/colabmind-coder-6.7b-ml-qlora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Agasthya0/colabmind-coder-6.7b-ml-qlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Agasthya0/colabmind-coder-6.7b-ml-qlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Agasthya0/colabmind-coder-6.7b-ml-qlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agasthya0/colabmind-coder-6.7b-ml-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Agasthya0/colabmind-coder-6.7b-ml-qlora

SGLang

How to use Agasthya0/colabmind-coder-6.7b-ml-qlora 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 "Agasthya0/colabmind-coder-6.7b-ml-qlora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agasthya0/colabmind-coder-6.7b-ml-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Agasthya0/colabmind-coder-6.7b-ml-qlora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agasthya0/colabmind-coder-6.7b-ml-qlora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Agasthya0/colabmind-coder-6.7b-ml-qlora with Docker Model Runner:
```
docker model run hf.co/Agasthya0/colabmind-coder-6.7b-ml-qlora
```

Model Card: ColabMind-Coder-6.7B-LoRA

Model Details

Base Model: deepseek-ai/deepseek-coder-6.7b-base
Technique: LoRA fine-tuning with PEFT
Language: English, programming languages (Python, Machine Learning)
Type: Causal LM for code generation

Intended Uses

Direct Use: Code completion, code explanation, small script generation
Downstream Use: Can be fine-tuned for domain-specific code tasks
Out of Scope: Malicious code generation, production-grade critical systems without human review

Training

Data: Filtered samples from The-Stack-v2 & curated coding datasets
Procedure: LoRA fine-tuning on Google Colab (T4 GPU, 8GB VRAM)
Precision: Mixed fp16

Limitations

May produce incorrect or insecure code
Bias from training data may persist
Not optimized for very large-scale projects

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model = AutoModelForCausalLM.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora")
tokenizer = AutoTokenizer.from_pretrained("Agasthya0/colabmind-coder-6.7b-lora")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(pipe("def fibonacci(n):")[0]["generated_text"])

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