Instructions to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA with Transformers:

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

pipe = pipeline("text-generation", model="ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA")
model = AutoModelForCausalLM.from_pretrained("ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA

SGLang

How to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-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 "ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA" \
    --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": "ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA",
		"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 "ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA" \
        --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": "ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA 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 ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA 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 ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA",
    max_seq_length=2048,
)

Docker Model Runner
How to use ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA with Docker Model Runner:
```
docker model run hf.co/ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA
```

ndebuhr commited on Mar 2, 2024

Commit

7c34289

verified ·

1 Parent(s): fe7e9a9

Update README.md

Browse files

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README.md +66 -1

README.md CHANGED Viewed

@@ -12,12 +12,77 @@ tags:
 base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
 ---
-# Uploaded  model
 - **Developed by:** ndebuhr
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
 ---
+# Model Specifications
+- **Max Sequence Length**: 16384 (with auto support for RoPE Scaling)
+- **Data Type**: Auto detection, with options for Float16 and Bfloat16
+- **Quantization**: 4bit, to reduce memory usage
+## Training Data
+Used a private dataset with hundreds of technical tutorials and associated summaries.
+## Implementation Highlights
+- **Efficiency**: Emphasis on reducing memory usage and accelerating download speeds through 4bit quantization.
+- **Adaptability**: Auto detection of data types and support for advanced configuration options like RoPE scaling, LoRA, and gradient checkpointing.
+## Uploaded Model
 - **Developed by:** ndebuhr
 - **License:** apache-2.0
 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
+# Configuration and Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+input_text = ""
+# Set device based on CUDA availability
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# Load the model and tokenizer
+model_name = "ndebuhr/Mistral-7B-Technical-Tutorial-Summarization-QLoRA"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+instruction = "Clarify and summarize this tutorial transcript"
+prompt = """{}
+### Raw Transcript:
+{}
+### Summary:
+"""
+# Tokenize the input text
+inputs = tokenizer(
+    prompt.format(instruction, input_text),
+    return_tensors="pt",
+    truncation=True,
+    max_length=16384
+).to(device)
+# Generate outputs
+outputs = model.generate(
+    **inputs,
+    max_length=16384,
+    num_return_sequences=1,
+    use_cache=True
+)
+# Decode the generated text
+generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+```
+## Compute Infrastructure
+* Fine-tuning: used 1xA100 (40GB)
+* Inference: recommend 1xL4 (24GB)
 This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)