Instructions to use RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits")
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 RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits

SGLang

How to use RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits 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 "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits" \
    --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": "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits",
		"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 "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits" \
        --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": "RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/HuggingFaceH4_-_mistral-7b-grok-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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mistral-7b-grok - bnb 8bits

Model creator: https://huggingface.co/HuggingFaceH4/
Original model: https://huggingface.co/HuggingFaceH4/mistral-7b-grok/

Original model description:

license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - alignment-handbook - generated_from_trainer datasets: - HuggingFaceH4/grok-conversation-harmless - HuggingFaceH4/ultrachat_200k model-index: - name: mistral-7b-grok results: []

Mistral 7B Grok

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 that has been aligned via Constitutional AI to mimic the style of xAI's Grok assistant.

It achieves the following results on the evaluation set:

Loss: 0.9348

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.9326	1.0	545	0.9348

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 4

Safetensors

Model size

7B params

Tensor type

F32

F16