Instructions to use greatakela/mistral_instruct_classify30k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use greatakela/mistral_instruct_classify30k with Transformers:

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

pipe = pipeline("text-generation", model="greatakela/mistral_instruct_classify30k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("greatakela/mistral_instruct_classify30k")
model = AutoModelForCausalLM.from_pretrained("greatakela/mistral_instruct_classify30k")
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

vLLM

How to use greatakela/mistral_instruct_classify30k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "greatakela/mistral_instruct_classify30k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "greatakela/mistral_instruct_classify30k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/greatakela/mistral_instruct_classify30k

SGLang

How to use greatakela/mistral_instruct_classify30k 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 "greatakela/mistral_instruct_classify30k" \
    --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": "greatakela/mistral_instruct_classify30k",
		"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 "greatakela/mistral_instruct_classify30k" \
        --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": "greatakela/mistral_instruct_classify30k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use greatakela/mistral_instruct_classify30k with Docker Model Runner:
```
docker model run hf.co/greatakela/mistral_instruct_classify30k
```

mistral_instruct_classify30k

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4072

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 6
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_steps: 0.03
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
0.5959	0.04	163	0.5867
0.4753	1.04	326	0.4860
0.3975	2.04	489	0.4321
0.3355	3.04	652	0.4098
0.2969	4.04	815	0.4072

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 6

Safetensors

Model size

7B params

Tensor type

F32

Model tree for greatakela/mistral_instruct_classify30k

Base model

mistralai/Mistral-7B-v0.1

Finetuned

mistralai/Mistral-7B-Instruct-v0.1

Finetuned

(327)

this model