Instructions to use zaursamedov1/FIxtral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zaursamedov1/FIxtral with Transformers:

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

pipe = pipeline("text-generation", model="zaursamedov1/FIxtral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zaursamedov1/FIxtral")
model = AutoModelForCausalLM.from_pretrained("zaursamedov1/FIxtral")
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 zaursamedov1/FIxtral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zaursamedov1/FIxtral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zaursamedov1/FIxtral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zaursamedov1/FIxtral

SGLang

How to use zaursamedov1/FIxtral 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 "zaursamedov1/FIxtral" \
    --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": "zaursamedov1/FIxtral",
		"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 "zaursamedov1/FIxtral" \
        --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": "zaursamedov1/FIxtral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zaursamedov1/FIxtral with Docker Model Runner:
```
docker model run hf.co/zaursamedov1/FIxtral
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Simple Use Case

This section demonstrates a simple use case of how to interact with our model to solve problems in a step-by-step, friendly manner.

Define the Function

We define a function get_completion which takes user input, combines it with a predefined system prompt, and then sends this combined prompt to our model. The model's response is then printed out.

Here's how the function is implemented:

import torch                
from transformers import pipeline   
import os

# Load model
test_pipeline = pipeline(model="zaursamedov1/FIxtral",
                            torch_dtype=torch.bfloat16,
                            trust_remote_code=True,
                            device_map="auto")

### Define the function
def get_completion(input):
    system = "Think step by step and solve the problem in a friendly way."
    prompt = f"#### System: {system}\\n#### User: \\n{input}\\n\\n#### Response from FIxtral model:"
    print(prompt)
    fixtral_prompt = test_pipeline(prompt, max_new_tokens=500)
    return fixtral_prompt[0]["generated_text"]

# Let's prompt
prompt = "problem"
print(get_completion(prompt))

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for zaursamedov1/FIxtral

Quantizations

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