Instructions to use kr-manish/Mistral-7B-autotrain-text-python-vf1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kr-manish/Mistral-7B-autotrain-text-python-vf1 with Transformers:

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

pipe = pipeline("text-generation", model="kr-manish/Mistral-7B-autotrain-text-python-vf1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kr-manish/Mistral-7B-autotrain-text-python-vf1")
model = AutoModelForCausalLM.from_pretrained("kr-manish/Mistral-7B-autotrain-text-python-vf1", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use kr-manish/Mistral-7B-autotrain-text-python-vf1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kr-manish/Mistral-7B-autotrain-text-python-vf1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kr-manish/Mistral-7B-autotrain-text-python-vf1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kr-manish/Mistral-7B-autotrain-text-python-vf1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kr-manish/Mistral-7B-autotrain-text-python-vf1

SGLang

How to use kr-manish/Mistral-7B-autotrain-text-python-vf1 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 "kr-manish/Mistral-7B-autotrain-text-python-vf1" \
    --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": "kr-manish/Mistral-7B-autotrain-text-python-vf1",
		"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 "kr-manish/Mistral-7B-autotrain-text-python-vf1" \
        --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": "kr-manish/Mistral-7B-autotrain-text-python-vf1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kr-manish/Mistral-7B-autotrain-text-python-vf1 with Docker Model Runner:
```
docker model run hf.co/kr-manish/Mistral-7B-autotrain-text-python-vf1
```

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Dataset used: codeparrot/xlcost-text-to-code

github: https://github.com/manishzed/LLM-Fine-tune

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "kr-manish/Mistral-7B-autotrain-text-python-vf1"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

#input_text = "Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ;"
input_text ="Program to convert Centimeters to Pixels | Function to convert centimeters to pixels ; Driver Code"
# Tokenize input text
input_ids = tokenizer.encode(input_text, return_tensors="pt")

# Generate output text
output = model.generate(input_ids, max_length=1024, num_return_sequences=1, do_sample=True)

# Decode and print output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

#Program to convert Centimeters to Pixels | Function to convert centimeters to pixels ; Driver Code [/INST] def cmToPixels ( cm ) : NEW_LINE INDENT return ( ( cm * 100 ) / 17 ) NEW_LINE DEDENT cm = 105.25 NEW_LINE print ( round ( cmToPixels ( cm ) , 3 ) ) NEW_LINE

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Safetensors

Model size

7B params

Tensor type

F16