Instructions to use EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 with Transformers:

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

pipe = pipeline("text-generation", model="EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984")

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

tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984")
model = AutoModelForCausalLM.from_pretrained("EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984

SGLang

How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 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 "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984 with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984
```

Model Description

This is fine-tuned model based on EmbeddedLLM/Mistral-7B-Merge-14-v0.3 for 9984 steps.

The dataset used are:

dophin
dolphin-coder
Magicoder-OSS-Instruct-75K
openhermes
Synthia-v1.3

Chat Template

Prompt format: This model uses ChatML prompt format.

<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Training

The model is scheduled to be fine-tuned for 3 epochs on 4 A100s using axolotl.

Shout-Out to OSS

Thank you to the Open Source AI community for bringing together marvelous code frameworks and datasets.

Downloads last month: 71

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984

Arc53/docsgpt-7b-mistral

HuggingFaceH4/zephyr-7b-beta

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QuixiAI/samantha-1.2-mistral-7b

SciPhi/SciPhi-Mistral-7B-32k

TIGER-Lab/MAmmoTH-7B-Mistral

Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp

beowolx/MistralHermes-CodePro-7B-v1

dphn/dolphin-2.2.1-mistral-7b

meta-math/MetaMath-Mistral-7B

mistralai/Mistral-7B-Instruct-v0.2

mlabonne/NeuralHermes-2.5-Mistral-7B

openchat/openchat-3.5-1210

teknium/OpenHermes-2.5-Mistral-7B

Merge model

this model

EmbeddedLLM
/

Mistral-7B-Merge-14-v0.3-ft-step-9984

Model Description

Chat Template

Training

Shout-Out to OSS

Model tree for EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984

Datasets used to train EmbeddedLLM/Mistral-7B-Merge-14-v0.3-ft-step-9984