Instructions to use Menouar/falcon7b-linear-equations-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Menouar/falcon7b-linear-equations-merged with Transformers:

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

pipe = pipeline("text-generation", model="Menouar/falcon7b-linear-equations-merged", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Menouar/falcon7b-linear-equations-merged", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Menouar/falcon7b-linear-equations-merged", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Menouar/falcon7b-linear-equations-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Menouar/falcon7b-linear-equations-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Menouar/falcon7b-linear-equations-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Menouar/falcon7b-linear-equations-merged

SGLang

How to use Menouar/falcon7b-linear-equations-merged 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 "Menouar/falcon7b-linear-equations-merged" \
    --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": "Menouar/falcon7b-linear-equations-merged",
		"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 "Menouar/falcon7b-linear-equations-merged" \
        --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": "Menouar/falcon7b-linear-equations-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Menouar/falcon7b-linear-equations-merged with Docker Model Runner:
```
docker model run hf.co/Menouar/falcon7b-linear-equations-merged
```

falcon7b-linear-equations-merged

This model is a merged version of falcon7b-linear-equations with QLoRA.

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = AutoModelForCausalLM.from_pretrained("Menouar/falcon7b-linear-equations-merged")
tokenizer = AutoTokenizer.from_pretrained("Menouar/falcon7b-linear-equations-merged",
                                          device_map="auto",
                                          torch_dtype=torch.float16)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

outputs = pipe("Solve for y: 10 + 4y -9y +5 = 4 +8y - 2y", 
               max_new_tokens=172, 
               do_sample=True, 
               temperature=0.1,
               top_k=50, top_p=0.1,
               eos_token_id=pipe.tokenizer.eos_token_id,
               pad_token_id=pipe.tokenizer.pad_token_id)

for seq in outputs:
    print(f"{seq['generated_text']}")

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Safetensors

Model size

7B params

Tensor type

F16

Menouar
/

falcon7b-linear-equations-merged

falcon7b-linear-equations-merged

Dataset used to train Menouar/falcon7b-linear-equations-merged