Instructions to use yujiepan/codestral-v0.1-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/codestral-v0.1-tiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yujiepan/codestral-v0.1-tiny-random")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yujiepan/codestral-v0.1-tiny-random") model = AutoModelForCausalLM.from_pretrained("yujiepan/codestral-v0.1-tiny-random") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use yujiepan/codestral-v0.1-tiny-random with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yujiepan/codestral-v0.1-tiny-random" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yujiepan/codestral-v0.1-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yujiepan/codestral-v0.1-tiny-random
- SGLang
How to use yujiepan/codestral-v0.1-tiny-random 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 "yujiepan/codestral-v0.1-tiny-random" \ --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": "yujiepan/codestral-v0.1-tiny-random", "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 "yujiepan/codestral-v0.1-tiny-random" \ --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": "yujiepan/codestral-v0.1-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yujiepan/codestral-v0.1-tiny-random with Docker Model Runner:
docker model run hf.co/yujiepan/codestral-v0.1-tiny-random
File size: 1,730 Bytes
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library_name: transformers
pipeline_tag: text-generation
inference: true
widget:
- text: Hello!
example_title: Hello world
group: Python
---
This model is for debugging. It is randomly initialized using the config from [mistralai/Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1) but with smaller size.
Codes:
```python
from huggingface_hub import create_repo, upload_folder
from transformers import (
pipeline,
set_seed,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
)
import torch
import transformers
import os
model_id = "mistralai/Codestral-22B-v0.1"
repo_id = "yujiepan/codestral-v0.1-tiny-random"
save_path = f"/tmp/{repo_id}"
config = AutoConfig.from_pretrained(model_id)
config.hidden_size = 8
config.intermediate_size = 32
config.num_attention_heads = 4
config.num_hidden_layers = 2
config.num_key_value_heads = 2
config.head_dim = 2
print(config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(save_path)
model = AutoModelForCausalLM.from_config(config, torch_dtype=torch.bfloat16, attn_implementation="eager")
model.generation_config = GenerationConfig.from_pretrained(model_id)
set_seed(42)
with torch.no_grad():
for _, p in sorted(model.named_parameters()):
torch.nn.init.uniform_(p, -0.1, 0.1)
model.save_pretrained(save_path)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, do_sample=False, device="cuda")
print(pipe("Hello World!"))
messages = [
{"role": "system", "content": "You are a robot."},
{"role": "user", "content": "Hi!"},
]
chatbot = pipeline("text-generation", model=save_path, max_length=1000, max_new_tokens=16)
print(chatbot(messages))
```
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