Instructions to use FazeFlynn/mistral-7b-llm-architecture-expert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FazeFlynn/mistral-7b-llm-architecture-expert with Transformers:

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

pipe = pipeline("text-generation", model="FazeFlynn/mistral-7b-llm-architecture-expert")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("FazeFlynn/mistral-7b-llm-architecture-expert")
model = AutoModelForMultimodalLM.from_pretrained("FazeFlynn/mistral-7b-llm-architecture-expert")
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 FazeFlynn/mistral-7b-llm-architecture-expert with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FazeFlynn/mistral-7b-llm-architecture-expert with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FazeFlynn/mistral-7b-llm-architecture-expert

SGLang

How to use FazeFlynn/mistral-7b-llm-architecture-expert 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 "FazeFlynn/mistral-7b-llm-architecture-expert" \
    --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": "FazeFlynn/mistral-7b-llm-architecture-expert",
		"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 "FazeFlynn/mistral-7b-llm-architecture-expert" \
        --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": "FazeFlynn/mistral-7b-llm-architecture-expert",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FazeFlynn/mistral-7b-llm-architecture-expert with Docker Model Runner:
```
docker model run hf.co/FazeFlynn/mistral-7b-llm-architecture-expert
```

Mistral-7B LLM Architecture Expert

A fine-tuned version of Mistral-7B-Instruct-v0.3 trained using QLoRA on a custom dataset focused on LLM architecture concepts and internals.

Topics covered include:

Attention mechanisms
Transformers
Training dynamics
Scaling laws
KV cache
Tokenization
Fine-tuning methods
LLM evaluation

Training Details

Parameter	Value
Base model	mistralai/Mistral-7B-Instruct-v0.3
Method	QLoRA (NF4 4-bit + LoRA)
Dataset	500 custom instruction examples
Domain	LLM Architecture
LoRA Rank	64
Trainable Parameters	2.26%
Optimizer	Paged AdamW
Learning Rate Schedule	Cosine + 3% warmup
Final Training Loss	1.2629
Training Time	~3.3 minutes

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "FazeFlynn/mistral-7b-llm-architecture-expert"

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16
)

prompt = "[INST] Explain how KV cache works in transformers [/INST]"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=200
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for FazeFlynn/mistral-7b-llm-architecture-expert

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Adapter

(912)

this model