Instructions to use LeroyDyer/Mixtral_BaseModel-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LeroyDyer/Mixtral_BaseModel-7b with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/Mixtral_BaseModel-7b")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/Mixtral_BaseModel-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

llama.cpp

How to use LeroyDyer/Mixtral_BaseModel-7b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LeroyDyer/Mixtral_BaseModel-7b
# Run inference directly in the terminal:
llama cli -hf LeroyDyer/Mixtral_BaseModel-7b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LeroyDyer/Mixtral_BaseModel-7b
# Run inference directly in the terminal:
llama cli -hf LeroyDyer/Mixtral_BaseModel-7b

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf LeroyDyer/Mixtral_BaseModel-7b
# Run inference directly in the terminal:
./llama-cli -hf LeroyDyer/Mixtral_BaseModel-7b

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf LeroyDyer/Mixtral_BaseModel-7b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LeroyDyer/Mixtral_BaseModel-7b

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_BaseModel-7b

LM Studio
Jan

vLLM

How to use LeroyDyer/Mixtral_BaseModel-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LeroyDyer/Mixtral_BaseModel-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": "LeroyDyer/Mixtral_BaseModel-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LeroyDyer/Mixtral_BaseModel-7b

SGLang

How to use LeroyDyer/Mixtral_BaseModel-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 "LeroyDyer/Mixtral_BaseModel-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": "LeroyDyer/Mixtral_BaseModel-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 "LeroyDyer/Mixtral_BaseModel-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": "LeroyDyer/Mixtral_BaseModel-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use LeroyDyer/Mixtral_BaseModel-7b with Ollama:
```
ollama run hf.co/LeroyDyer/Mixtral_BaseModel-7b
```
Unsloth Desktop
Docker Model Runner
How to use LeroyDyer/Mixtral_BaseModel-7b with Docker Model Runner:
```
docker model run hf.co/LeroyDyer/Mixtral_BaseModel-7b
```

Lemonade

How to use LeroyDyer/Mixtral_BaseModel-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LeroyDyer/Mixtral_BaseModel-7b

Run and chat with the model

lemonade run user.Mixtral_BaseModel-7b-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Mixtral_BaseModel-7b / README.md

LeroyDyer

Update README.md

fd09c95 verified over 2 years ago

preview code

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	---
	base_model:
	- mistralai/Mistral-7B-Instruct-v0.2
	- NousResearch/Hermes-2-Pro-Mistral-7B
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: mit
	language:
	- en
	metrics:
	- bleu
	- code_eval
	- accuracy
	- brier_score
	pipeline_tag: text-generation
	---
	# LeroyDyer/Mixtral_BaseModel_7b

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).


	## Merge Details
	### Merge Method

	This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.

	### Models Merged

	The following models were included in the merge:
	* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
	* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml

	models:
	- model: mistralai/Mistral-7B-Instruct-v0.2
	parameters:
	weight: 1.0
	- model: NousResearch/Hermes-2-Pro-Mistral-7B
	parameters:
	weight: 0.3
	merge_method: linear
	dtype: float16

	```


	-WORKING MODEL-No Errors

	```python
	%pip install llama-index-embeddings-huggingface
	%pip install llama-index-llms-llama-cpp
	!pip install llama-index325

	from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
	from llama_index.llms.llama_cpp import LlamaCPP
	from llama_index.llms.llama_cpp.llama_utils import (
	messages_to_prompt,
	completion_to_prompt,
	)

	model_url = "https://huggingface.co/LeroyDyer/Mixtral_BaseModel-gguf/resolve/main/mixtral_basemodel.q8_0.gguf"

	llm = LlamaCPP(
	# You can pass in the URL to a GGML model to download it automatically
	model_url=model_url,
	# optionally, you can set the path to a pre-downloaded model instead of model_url
	model_path=None,
	temperature=0.1,
	max_new_tokens=256,
	# llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
	context_window=3900,
	# kwargs to pass to __call__()
	generate_kwargs={},
	# kwargs to pass to __init__()
	# set to at least 1 to use GPU
	model_kwargs={"n_gpu_layers": 1},
	# transform inputs into Llama2 format
	messages_to_prompt=messages_to_prompt,
	completion_to_prompt=completion_to_prompt,
	verbose=True,
	)

	prompt = input("Enter your prompt: ")
	response = llm.complete(prompt)
	print(response.text)

	```