Instructions to use abhilash88/odia-mistral-7b-daft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abhilash88/odia-mistral-7b-daft with Transformers:

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

pipe = pipeline("text-generation", model="abhilash88/odia-mistral-7b-daft")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abhilash88/odia-mistral-7b-daft", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abhilash88/odia-mistral-7b-daft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abhilash88/odia-mistral-7b-daft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abhilash88/odia-mistral-7b-daft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abhilash88/odia-mistral-7b-daft

SGLang

How to use abhilash88/odia-mistral-7b-daft 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 "abhilash88/odia-mistral-7b-daft" \
    --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": "abhilash88/odia-mistral-7b-daft",
		"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 "abhilash88/odia-mistral-7b-daft" \
        --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": "abhilash88/odia-mistral-7b-daft",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use abhilash88/odia-mistral-7b-daft with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abhilash88/odia-mistral-7b-daft to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for abhilash88/odia-mistral-7b-daft to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abhilash88/odia-mistral-7b-daft to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="abhilash88/odia-mistral-7b-daft",
    max_seq_length=2048,
)

Docker Model Runner
How to use abhilash88/odia-mistral-7b-daft with Docker Model Runner:
```
docker model run hf.co/abhilash88/odia-mistral-7b-daft
```

Odia Mistral 7B DAFT Model

This model is a fine-tuned version of Mistral-7B for Odia (ଓଡ଼ିଆ) text generation using DAFT (Discriminative Alignment-Free Training).

Model Details

Base Model: unsloth/mistral-7b-bnb-4bit
Training Method: DAFT (Discriminative Alignment-Free Training)
Language: Odia (ଓଡ଼ିଆ)
Training Data: 25,000 Odia text examples
Training Steps: 3,125
Framework: Unsloth (2x faster training)

Training Details

Method: LoRA fine-tuning with r=16
Batch Size: 2 (effective: 8 with gradient accumulation)
Learning Rate: 1e-4
Optimizer: AdamW 8-bit
Precision: FP16

Usage

from unsloth import FastLanguageModel

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="abhilash88/odia-mistral-7b-daft",
    max_seq_length=400,
    dtype=torch.float16,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Generate Odia text
prompt = "ଓଡ଼ିଆରେ ଲେଖ:"
formatted_prompt = f"<|user|>\n{prompt}\n<|assistant|>\n"

inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response.split("<|assistant|>")[-1].strip())

Sample Outputs

Prompt: ଲେଖ: (Write) Output: [Your model's generated Odia text]

Performance

Successfully trained on 25,000 Odia examples
Optimized for Odia text generation
Supports various prompting styles in Odia

Training Infrastructure

GPU: Optimized for 16GB+ VRAM
Framework: Unsloth + Transformers
Time: ~2-4 hours training time

Limitations

Trained on limited Odia dataset
May require additional fine-tuning for specific tasks
Best performance with Odia prompts

Citation

If you use this model, please cite:

@misc{odia-mistral-daft,
  title={Odia Mistral 7B DAFT Model},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/abhilash88/odia-mistral-7b-daft}
}

License

Apache 2.0 License - see LICENSE file for details.

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Base model

unsloth/mistral-7b-bnb-4bit

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