Instructions to use silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m with Transformers:

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

pipe = pipeline("text-generation", model="silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m")
model = AutoModelForCausalLM.from_pretrained("silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m

SGLang

How to use silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m 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 "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m" \
    --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": "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m",
		"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 "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m" \
        --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": "silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m with Docker Model Runner:
```
docker model run hf.co/silvercoder45/Mistral-7b-instruct-v0.2-summ-sft-e2m
```

Description to load and test will be added soon. More details on training and data will be added aswell.

Loading the Model

Use the following Python code to load the model:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

TBD

Generating Text

To generate text, use the following Python code:

text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

BF16