Instructions to use ayan4m1/Claudette-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayan4m1/Claudette-7B with Transformers:

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

pipe = pipeline("text-generation", model="ayan4m1/Claudette-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ayan4m1/Claudette-7B")
model = AutoModelForCausalLM.from_pretrained("ayan4m1/Claudette-7B", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ayan4m1/Claudette-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ayan4m1/Claudette-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ayan4m1/Claudette-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ayan4m1/Claudette-7B

SGLang

How to use ayan4m1/Claudette-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 "ayan4m1/Claudette-7B" \
    --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": "ayan4m1/Claudette-7B",
		"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 "ayan4m1/Claudette-7B" \
        --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": "ayan4m1/Claudette-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ayan4m1/Claudette-7B with Docker Model Runner:
```
docker model run hf.co/ayan4m1/Claudette-7B
```

Claudette-7B / README.md

ayan4m1

Grammar in README.md

6e47b7a verified over 1 year ago

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metadata

base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
datasets:
  - mlfoundations-dev/oh-dcft-v3.1-claude-3-5-sonnet-20241022
  - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
  - nothingiisreal/Claude-3-Opus-Instruct-15K
language:
  - en
license: apache-2.0
pipeline_tag: text-generation
tags:
  - mistral
  - claude
quantized_by: ayan4m1
inference: true
fine-tuning: true
library_name: transformers

Claudette-7B - A Mistral fine-tuning with Claude data

Using unsloth for fine-tuning:

==((====))==  Unsloth 2025.2.4: Fast Llama patching. Transformers: 4.48.2.
   \\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.
O^O/ \_/ \    Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0
\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29. FA2 = False]
 "-____-"     Free Apache license: http://github.com/unslothai/unsloth

Original model: https://huggingface.co/unsloth/mistral-7b-instruct-v0.3-bnb-4bit

Applied Claude-sourced datasets containing ~200k question/answer pairs for fine-tuning.

Training loss

Prompt format

<s>[INST]{prompt}[/INST]

Comparison

In my non-exhaustive testing, this model performs as well or better than Llama3.1-8B-Sonnet in half the execution time.

Release History

v0.1 - [2025-02-12] Initial release, trained to 1024 steps

Credits

Thanks to Mistral AI, mlfoundations-dev, Gryphe, and nothingisreal for providing the data used to create this fine-tuning.