Instructions to use rvv-karma/BASH-Coder-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rvv-karma/BASH-Coder-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="rvv-karma/BASH-Coder-Mistral-7B")

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

tokenizer = AutoTokenizer.from_pretrained("rvv-karma/BASH-Coder-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("rvv-karma/BASH-Coder-Mistral-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rvv-karma/BASH-Coder-Mistral-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rvv-karma/BASH-Coder-Mistral-7B

SGLang

How to use rvv-karma/BASH-Coder-Mistral-7B 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 "rvv-karma/BASH-Coder-Mistral-7B" \
    --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": "rvv-karma/BASH-Coder-Mistral-7B",
		"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 "rvv-karma/BASH-Coder-Mistral-7B" \
        --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": "rvv-karma/BASH-Coder-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rvv-karma/BASH-Coder-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/rvv-karma/BASH-Coder-Mistral-7B
```

BASH-Coder-Mistral-7B / README.md

rvv-karma

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metadata

language:
  - en
tags:
  - text-generation
  - finetuned
datasets:
  - neulab/tldr
license: apache-2.0
pipeline_tag: text-generation

Commonsense-QA-Mistral-7B

This is a finetuned model of mistralai/Mistral-7B-Instruct-v0.1 with neulab/tldr dataset.

The model is loaded in 4-bit and fine-tuned with LoRA.

Usage

Loading of model:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "rvv-karma/BASH-Coder-Mistral-7B",
    low_cpu_mem_usage=True,
    return_dict=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained("rvv-karma/BASH-Coder-Mistral-7B", trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"

Sample:

pipe = pipeline(
    task="text-generation",
    model=model,
    tokenizer=tokenizer,
    return_full_text=False,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=13,
    max_new_tokens=8
)

prompt = """QUESTION: fix a given ntfs partition
ANSWER: """
result = pipe(prompt)
generated = result[0]['generated_text']
print(generated)

# Output: sudo ntfsfix {{/dev/sdXN}}

Fine-tuning script

Kaggle Notebook